The distribution of video content is nowadays not only possible via the traditional broadcast channels (terrestric antenna/satellite/cable), but also via internet or data based services. In both distribution systems the content may suffer a loss of quality due to limited bandwidth and/or storage capacity. Especially in some internet based video services as video portals (e.g. YouTube™) the allowed data rate and storage capacity is very limited. Thus, the resolution and frame rate of the distributed video content may be quite low. Furthermore, lossy source coding schemes may be applied to the video content (e.g. MPEG2, H.263, MPEG4 Video, etc.), which also negatively affect the video quality and lead to losses of some essential information (e.g. textures or details).
A lot of source coding schemes are based on the idea to divide an image into several blocks and transform each block separately to separate relevant from redundant information. Only relevant information is transmitted or stored. A widely used transformation is the discrete cosine transform (DCT). As two consecutive frames in a video scene do in most cases not differ too much, the redundancy in the temporal direction may be reduced by transmitting or storing only differences between frames. The impact of such lossy coding schemes may be visible in the decoded video if some relevant information is not transmitted or stored. These visible errors are called (coding) artifacts.
There are some typical coding artifacts in block based DCT coding schemes. The most obvious artifact is blocking: The periodic block raster of the block based transform becomes visible as a pattern, sometimes with high steps in amplitude at the block boundaries. A second artifact is caused by lost detail information and is visible as periodic variations across object edges in the video content (ringing). A varying ringing in consecutive frames of an image sequence at object edges may be visible as a sort of flicker or noise (mosquito noise).
Coding artifacts are not comparable to conventional errors such as additive Gaussian noise. Therefore, conventional techniques for error reduction and image enhancement may not be directly transferred to coding artifact reduction. While blocking is nowadays reduced by adaptive low-pass filters at block boundaries (either in-the-loop while decoding or as post-processing on the decoded image or video), ringing and mosquito noise are more difficult to reduce, since the applied filtering must not lower the steepness of edges in the image content.
One of the main tasks of an adequate method for artifact reduction is the preservation of details while the artifacts ought to be strongly reduced. Therefore, the area where the artifacts occur should be strongly filtered, while in textured areas the details should not be removed by a too strong filtering. As coding artifacts often have similar characteristics as textures, the detection of these areas is not straightforward.